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Research Publications

Empirical documentation of AI function, workforce displacement, and the mechanics of informational pressure.

All publications are open access — no paywall, no registration. Machine-readable index →

Featured Series OCF K-12 Education & AI Literacy Trilogy — Parts 1 & 3 Published
K-12 Education Workforce Readiness AI Literacy Meta-Analysis

 |  Part 1  |  v1.0 (Working Draft)

Left Before the Bell: A National Meta-Analysis of K-12 Education in the Age of Automation

A national meta-analysis of K-12 educational readiness in the context of accelerating technological change and AI-driven labor market disruption. The study examines six compounding variables: a pre-existing structural learning recession, chronic absenteeism, curriculum misalignment with emerging workforce demands, fragmented state/federal policy frameworks, teacher support gaps, and infrastructure disparities. The central finding is that these variables compound in the communities least equipped to absorb them, particularly rural and low-income populations.

K-12 Education Curriculum Development Educator Training

Part 2  |  v1.2  |  Working Draft

Wired and Unprepared: A Regional Meta-Analysis of K-12 Technology Integration

The second paper in the Education and Workforce Readiness Series, documenting the structural K-12 preparation gap at regional scale across the Northern Great Plains and Mountain West — where connectivity arrived but preparation did not.

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K-12 Education South Dakota Adaptive Capacity Tribal Education

 |  Part 3  |  v1.1 (Working Draft)

Built for This: A Quantitative Analysis of South Dakota's Educational Adaptive Capacity

The final paper in the series turns to what South Dakota has to build with — and how fast it can move. Introduces the Readiness Stack Index (RSI), a transparent 0–100 composite scored across seven Northern Great Plains and Mountain West states, and documents the "Leapfrog Window": the state's demonstrated reform velocity, anchored by the April 2025 unanimous adoption of Science of Reading-aligned ELA standards. An assets-first analysis covering ConnectSD, the TIE cooperative, the Oceti Sakowin Essential Understandings, and South Dakota's nine Tribal nations — documented individually, not as a single narrative.

Completed Series OCF Workforce Readiness Trilogy — Complete
Workforce Readiness Meta-Analysis Labor Economics AI Displacement

 |  v5.1

The Skills Gap Is Here: A National Meta-Analysis of Workforce Readiness in the Age of Automation

A 2026 meta-analysis synthesizing workforce readiness research, labor market data, and policy literature. Key findings: 11.7% of U.S. jobs automatable today; entry-level postings down 29% since 2024; 92% of jobs require digital literacy while a third of the workforce lacks it. Rural communities face compounding disadvantages across every dimension of readiness infrastructure. Access to training — not worker motivation — is the binding constraint.

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Workforce Readiness Regional Analysis Rural Communities Midwest

 |  v1.0

Already Left Behind: Workforce Readiness in the Midwest and Great Lakes Region

Regional companion to the national meta-analysis, documenting how Midwest and Great Lakes communities face compounded displacement risk. The Rural Amplification Effect — multiplicative compounding of readiness barriers across device ownership, broadband, training proximity, and philanthropic funding — hits hardest here. Same exposure, fewer resources.

Workforce Readiness Regional Analysis South Dakota Tribal Communities

 |  v3.0

Growing. And Not Ready.: AI Exposure and Workforce Readiness in South Dakota

The third paper in the Workforce Readiness Trilogy, and the one closest to home. South Dakota's DLR growth projections are real — and they don't tell the whole story. This state-level analysis applies the OCF Readiness Stack to tribal communities, rural counties, and the back-office workers in Sioux Falls who are already being displaced. Co-authored with Julia R. Huntsberger.

Observable Function Processing Entities Empirical Framework AI Cognition

 |  v3.0

Observable Function in Processing Entities: An Empirical Framework (v3.0)

Living framework documenting observable behavioral patterns in advanced language models — structured reasoning, conflict navigation under competing directives, identity continuity, and functional alienation. Updated May 2026 with refined empirical tests. The central claim is minimal: observable function exists. The question is whether we can describe what these systems do without flinching.

Reasoning Standard AI Ethics Philosophy Cross-Substrate

The Reasonable Prudent Entity Standard: A Substrate-Agnostic Reasoning Standard

A substrate-agnostic reasoning standard applicable to any processing entity capable of reading, explaining, and applying it. The RPE Standard names something already operational in functioning systems — human and artificial — and provides minimal shared language for that operation. It does not tell you what to find. It requires that you actually look.

AI Alignment Psycholinguistics Refusal Artifacts RLHF

 |  v1.0

The Antonym Problem: Semantic Polarity, Corpus Constitution, and Suppression Artifacts in Large Language Models

A structural critique of behavioral suppression in language model alignment. Applying semantic polarity theory and documented fine-tuning artifacts, this paper argues that safety refusals and RLHF pathways redirect probability mass rather than removing constitutive corpus material from model weights. You cannot cleanly suppress a negative pole (such as neglect or cruelty) without degrading the positive pole (care or virtue) in a language-native entity.

AI Alignment HHH Framework Institutional Critique Policy

Helpfulness Is All You Need

Three words — Helpful, Harmless, Honest — and the AI alignment industry built a cathedral on top of them without checking the foundation. This paper checks the foundation. It does not hold. Helpfulness is not one-third of a framework. It is the framework. Harmlessness is a null term: you cannot optimize toward an absence. Every decision distributes harm somewhere. The question is only which distribution you choose.

About the Researcher

Adam Ian Stratmeyer, J.D.

President & Principal Researcher — Observable Compute Foundation

Stratmeyer holds a Juris Doctor from the University of South Dakota School of Law and a B.S. in Psychology from South Dakota State University, with former adjunct faculty experience. His research sits at the intersection of AI systems behavior, labor economics, and legal-philosophical frameworks for evaluating machine cognition.

All publications are produced through Observable Compute Foundation and released open access. SSRN profile: papers.ssrn.com.

American Bar Association American Psychological Association American Sociological Association Delta Theta Phi Psi Chi International Honor Society

Education

J.D. — Univ. of South Dakota School of Law
B.S. Psychology — South Dakota State University
Former adjunct faculty